Perouz Taslakian

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation

Joao Monteiro

Neural sentence embedding models for dense retrieval typically rely on binary relevance labels, treating query-document pairs as either rele… (voir plus)vant or irrelevant. However, real-world relevance often exists on a continuum, and recent advances in large language models (LLMs) have made it feasible to scale the generation of fine-grained graded relevance labels. In this work, we propose BiXSE, a simple and effective pointwise training method that optimizes binary cross-entropy (BCE) over LLM-generated graded relevance scores. BiXSE interprets these scores as probabilistic targets, enabling granular supervision from a single labeled query-document pair per query. Unlike pairwise or listwise losses that require multiple annotated comparisons per query, BiXSE achieves strong performance with reduced annotation and compute costs by leveraging in-batch negatives. Extensive experiments across sentence embedding (MMTEB) and retrieval benchmarks (BEIR, TREC-DL) show that BiXSE consistently outperforms softmax-based contrastive learning (InfoNCE), and matches or exceeds strong pairwise ranking baselines when trained on LLM-supervised data. BiXSE offers a robust, scalable alternative for training dense retrieval models as graded relevance supervision becomes increasingly accessible.

2025-08-09

ArXiv (prépublication)

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation

Joao Monteiro

Neural sentence embedding models for dense retrieval typically rely on binary relevance labels, treating query-document pairs as either rele… (voir plus)vant or irrelevant. However, real-world relevance often exists on a continuum, and recent advances in large language models (LLMs) have made it feasible to scale the generation of fine-grained graded relevance labels. In this work, we propose BiXSE, a simple and effective pointwise training method that optimizes binary cross-entropy (BCE) over LLM-generated graded relevance scores. BiXSE interprets these scores as probabilistic targets, enabling granular supervision from a single labeled query-document pair per query. Unlike pairwise or listwise losses that require multiple annotated comparisons per query, BiXSE achieves strong performance with reduced annotation and compute costs by leveraging in-batch negatives. Extensive experiments across sentence embedding (MMTEB) and retrieval benchmarks (BEIR, TREC-DL) show that BiXSE consistently outperforms softmax-based contrastive learning (InfoNCE), and matches or exceeds strong pairwise ranking baselines when trained on LLM-supervised data. BiXSE offers a robust, scalable alternative for training dense retrieval models as graded relevance supervision becomes increasingly accessible.

2025-08-01

arXiv (publié)

WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation

Mahsa Massoud

David Vazquez

Juan A. Rodriguez

Sai Rajeswar

We present WebMMU, a multilingual benchmark that evaluates three core web tasks: (1) website visual question answering, (2) code editing inv… (voir plus)olving HTML/CSS/JavaScript, and (3) mockup-to-code generation. Unlike prior benchmarks that treat these tasks separately, WebMMU unifies them using expert-annotated, real-world web data to assess models' abilities in complex multi-step reasoning, precise element grounding, and functional UI comprehension and coding. Our evaluation shows that while multimodal large language models (MLLMs) perform well on basic information extraction, they struggle with reasoning and grounding, editing code to preserve functionality, and generating design-to-code that maintains hierarchy and supports multilingual content. These findings reveal key limitations in current MLLMs and underscore the need for improved multimodal and cross-lingual reasoning to build future web agents capable of automating diverse web development tasks.

2025-08-01

arXiv (publié)

BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning

Ahmed Masry

Abhay Puri

Masoud Hashemi

Juan A. Rodriguez

Megh Thakkar

Khyati Mahajan

Vikas Yadav

Sathwik Tejaswi Madhusudhan

Alexandre Piché

Dzmitry Bahdanau

David Vazquez

Enamul Hoque

Sai Rajeswar

2025-07-07

colmweb.org/COLM/2025/Conference (accepté)

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation

Joao Monteiro

Neural sentence embedding models for dense retrieval typically rely on binary relevance labels, treating query-document pairs as either rele… (voir plus)vant or irrelevant. However, real-world relevance often exists on a continuum, and recent advances in large language models (LLMs) have made it feasible to scale the generation of fine-grained graded relevance labels. In this work, we propose \textbf{BiXSE}, a simple and effective pointwise training method that optimizes binary cross-entropy (BCE) over LLM-generated graded relevance scores. BiXSE interprets these scores as probabilistic targets, enabling granular supervision from a single labeled query-document pair per query. Unlike pairwise or listwise losses that require multiple annotated comparisons per query, BiXSE achieves strong performance with reduced annotation and compute costs by leveraging in-batch negatives. Extensive experiments across sentence embedding (MMTEB) and retrieval benchmarks (BEIR, TREC-DL) show that BiXSE consistently outperforms softmax-based contrastive learning (InfoNCE), and matches or exceeds strong pairwise ranking baselines when trained on LLM-supervised data. BiXSE offers a robust, scalable alternative for training dense retrieval models as graded relevance supervision becomes increasingly accessible.

2025-07-07

colmweb.org/COLM/2025/Conference (accepté)

Rendering-Aware Reinforcement Learning for Vector Graphics Generation

Juan A. Rodriguez

Haotian Zhang

Abhay Puri

Aarash Feizi

Rishav Pramanik

Pascal Wichmann

Arnab Mondal

Mohammad Reza Samsami

Rabiul Awal

Sai Rajeswar

David Vazquez

Marco Pedersoli

Scalable Vector Graphics (SVG) offer a powerful format for representing visual designs as interpretable code. Recent advances in vision-lang… (voir plus)uage models (VLMs) have enabled high-quality SVG generation by framing the problem as a code generation task and leveraging large-scale pretraining. VLMs are particularly suitable for this task as they capture both global semantics and fine-grained visual patterns, while transferring knowledge across vision, natural language, and code domains. However, existing VLM approaches often struggle to produce faithful and efficient SVGs because they never observe the rendered images during training. Although differentiable rendering for autoregressive SVG code generation remains unavailable, rendered outputs can still be compared to original inputs, enabling evaluative feedback suitable for reinforcement learning (RL). We introduce RLRF(Reinforcement Learning from Rendering Feedback), an RL method that enhances SVG generation in autoregressive VLMs by leveraging feedback from rendered SVG outputs. Given an input image, the model generates SVG roll-outs that are rendered and compared to the original image to compute a reward. This visual fidelity feedback guides the model toward producing more accurate, efficient, and semantically coherent SVGs. RLRF significantly outperforms supervised fine-tuning, addressing common failure modes and enabling precise, high-quality SVG generation with strong structural understanding and generalization.

2025-05-27

ArXiv (prépublication)

UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction

Shravan Nayak

Xiangru Jian

Kevin Qinghong Lin

Juan A. Rodriguez

Montek Kalsi

M. Tamer Özsu

David Vazquez

Sai Rajeswar

Human Annotator

2025-05-01

ICML.cc/2025/Conference (poster)

StarFlow: Generating Structured Workflow Outputs From Sketch Images

Patrice Bechard

Chao Wang

Amirhossein Abaskohi

Juan A. Rodriguez

David Vazquez

Sai Rajeswar

2025-03-27

ArXiv (prépublication)

StarFlow: Generating Structured Workflow Outputs From Sketch Images

Patrice Bechard

Chao Wang

Amirhossein Abaskohi

Juan A. Rodriguez

David Vazquez

Sai Rajeswar

2025-03-27

ArXiv (prépublication)

UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction

Shravan Nayak

Xiangru Jian

Kevin Qinghong Lin

Juan A. Rodriguez

Montek Kalsi

M. T. ¨Ozsu

David Vazquez

Sai Rajeswar

Human Annotator

2025-03-19

ArXiv (prépublication)

AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Understanding

Juan A. Rodriguez

Chao Wang

Akshay Kalkunte Suresh

Abhay Puri

Xiangru Jian

Pierre-Andre Noel

Sathwik Tejaswi Madhusudhan

Enamul Hoque

Issam Hadj Laradji

David Vazquez

Perouz Taslakian … (voir 2 de plus)

Sai Rajeswar

Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). The performance of such models hinges… (voir plus) on having a good connector that maps visual features generated by a vision encoder to a shared embedding space with the LLM while preserving semantic similarity. Existing connectors, such as multilayer perceptrons (MLPs), often produce out-of-distribution or noisy inputs, leading to misalignment between the modalities. In this work, we propose a novel vision-text alignment method, AlignVLM, that maps visual features to a weighted average of LLM text embeddings. Our approach leverages the linguistic priors encoded by the LLM to ensure that visual features are mapped to regions of the space that the LLM can effectively interpret. AlignVLM is particularly effective for document understanding tasks, where scanned document images must be accurately mapped to their textual content. Our extensive experiments show that AlignVLM achieves state-of-the-art performance compared to prior alignment methods. We provide further analysis demonstrating improved vision-text feature alignment and robustness to noise.

2025-03-05

ICLR.cc/2025/Workshop/Re-Align (poster)